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Association analysis identifies 65 new breast cancer risk loci
Kyriaki Michailidou, Sara Lindström, Joe Dennis, Jonathan Beesley, Shirley Hui, Siddhartha Kar, Audrey Lemaçon, Penny Soucy, Dylan M. Glubb, Asha Rostamianfar, Manjeet K. Bolla, Qin Wang, Jonathan P. Tyrer, Ed Dicks, Andrew Lee, Zhaoming Wang, Jamie Allen, Renske Keeman, Ursula Eilber, Juliet D. French, Laura Fachal, Karen McCue, Amy E. McCart Reed, Maya Ghoussaini, Jason S. Carroll, Xia Jiang, Hilary K. Finucane, Marcia Adams, Muriel A. Adank, Habibul Ahsan, Kristiina Aittomäki, Hoda Anton‐Culver, Natalia Antonenkova, Volker Arndt, Kristan J. Aronson, Banu Arun, Paul L. Auer, François Bacot, Myrto Barrdahl, Caroline Baynes, Matthias W. Beckmann, Sabine Behrens, Javier Benı́tez, Marina Bermisheva, Leslie Bernstein, Carl Blomqvist, Natalia Bogdanova, Stig E. Bojesen, Bernardo Bonanni, Anne‐Lise Børresen‐Dale, Judith S. Brand, Hiltrud Brauch, Paul Brennan, Hermann Brenner, Louise A. Brinton, Per Broberg, Ian W. Brock, Annegien Broeks, Angela Brooks‐Wilson, Sara Y. 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Bibliographic record
VenueNature · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsUniversity of British ColumbiaUniversity Health NetworkPublic Health OntarioSimon Fraser UniversityMcGill UniversityLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalQueen's UniversityCentre Hospitalier de l’Université de MontréalUniversité LavalBC Cancer AgencyRoyal Victoria HospitalCentre hospitalier universitaire de QuébecMcGill University and Génome Québec Innovation CentreUniversity of Toronto
FundersNational Cancer InstituteNational Human Genome Research InstituteFogarty International CenterNational Institute of Mental HealthCanadian Institutes of Health ResearchMedical Research CouncilMinistero dello Sviluppo EconomicoWorld Health OrganizationEuropean CommissionNational Institutes of HealthCancer Research UKGovernment of CanadaFondation du cancer du sein du QuébecGenome CanadaFrancis Crick InstituteNational Institute of General Medical SciencesMinistère du Développement Économique, de l’Innovation et de l’ExportationNational Institute for Health and Care Research
KeywordsBreast cancerOncologyAssociation (psychology)MedicineGeneticsBiologyComputational biologyCancerPsychology
Abstract
OpenAlex records an abstract for this work, but it could not be fetched just now.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Opus teacher head0.006
GPT teacher head0.290
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".
Quick stats
Citations1,582
Published2017
Admission routes2
Has abstractyes